Mining model versioning

ABSTRACT

One implementation provides a computer system that allows front-end software applications to use multiple versions of a data mining model during execution of analytical tasks. In this implementation, the computer system includes a model version selection module that is operable to use a task name in a task request received from a front-end software application to determine a specific version of a data mining model to be used during execution of an analytical task. The computer system also includes a mapping module that is operable to map input data included in the task request received from the front-end software application into a format usable by the specific version of the data mining model.

RELATED APPLICATION

This application is a continuation-in-part application that claimspriority to application Ser. No. 10/454,370 that was filed on Jun. 3,2003.

TECHNICAL FIELD

This invention relates to computing systems that utilize data miningmodels.

BACKGROUND

In a real-time analytics system, various front-end software applicationsprovide customer transaction data directly to an analytical softwareapplication that is capable of executing analytical tasks. An example ofsuch an analytical software application is a prediction application thatprovides useful, predictive output relating to a transaction with acustomer. An analytical software application is capable of processingreal-time data from a customer to execute analytical tasks and togenerate output. In many instances, the analytical software applicationwill use the real-time data in coordination with a data mining model togenerate a predictive output. A data mining model is typically derivedfrom historical data that has been collected, synthesized, andformatted. In many instances, a predictive output generated uponexecution of an analytical task is fed into a business rule engine. Thebusiness rule engine will use the predictive output in conjunction withits rule set to determine if certain events should be triggered in agiven front-end application. For example, the business rule engine maydetermine that a special promotional offer should be provided to aparticular customer given the content of the predictive output and thenature of the transaction with that customer. In some instances, thefront-end applications may directly process the predictive output.

During operation of the real-time analytics system, new data needs to beconsidered by the data mining model. For example, a data mining modelthat is used to predict the customer churn probability must take intoaccount the latest behavior of customers. Therefore, new data must beused to “re-train,” or update, an existing data mining model. Theupdated model then can be used during the execution of subsequentanalytical tasks. As multiple front-end software applications may wantto use the same model, but with different timeliness of data used totrain the models, the applications have to deal with a large set ofdifferent mining model “versions.” For example, a front-end softwareapplication may want to use a first version of a mining model that istrained with customer data from 2002, but may later want to use a secondversion of the mining model that is trained with customer data from2003. Typically, however, front-end software applications maintaindirect interfaces to the analytical software applications that utilizethese models for predictions during task execution. In maintaining theseinterfaces, the front-end software applications often need to havedetailed knowledge of the specific types of analytical softwareapplications and data mining model versions that are used. For instance,a front-end application may need to provide specific input informationthat is specific to the model version used by the analytical softwareapplication when executing analytical tasks. In the example above, thefront-end application may need to provide different types of inputinformation depending on whether the first model version (trained withcustomer data from 2002) or the second model version (trained withcustomer data from 2003) is used.

SUMMARY

Various implementations of the invention are provided herein. Oneimplementation provides a computer system that allows front-end softwareapplications to use multiple versions of a data mining model duringexecution of analytical tasks. In this implementation, the computersystem includes a model version selection module that is operable to usea task name in a task request received from a front-end softwareapplication to determine a specific version of a data mining model to beused during execution of an analytical task. The computer system alsoincludes a mapping module that is operable to map input data included inthe task request received from the front-end software application into aformat usable by the specific version of the data mining model.

Certain implementations of the present invention may have manyadvantages. For example, front-end applications are able to easily andquickly switch between model versions that are to be utilized whenexecuting analytical tasks. In certain scenarios, these front-endapplications need only to provide a task name and one or more inputvalues used for execution of the task. In these scenarios, the front-endapplications do not need to specify the version of the model to be used,and also do not need to specify a mapping for input and outputparameters between the application and the model version to be used.Certain implementations of the invention, however, allow parametermappings to be customized to a specific model version. In theseimplementations, a task definition in an Analytical Application Provider(AAP) may specify particular mappings that are to be used for the inputor output parameters between the application and a given model version.These mappings, however, are transparent to the application.

The details of one or more implementations of the invention are setforth in the accompanying drawings and the description below. Otherfeatures, objects, and advantages of the invention will be apparent fromthe description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a block diagram of a computing system that utilizes multipleversions of a data mining model during execution of analytical tasks.

FIG. 1B is a block diagram of a computing system that incorporates thecomponents shown in FIG. 1A.

FIG. 2 is a use-case diagram of design- and run-time scenarios forvarious implementations.

FIG. 3A is a conceptual diagram of an architectural design that providesmapping functionality for prediction and key performance indicator (KPI)lookup tasks.

FIG. 3B is a conceptual diagram of an architectural design for usingmultiple versions of data mining models, according to oneimplementation.

FIG. 4 is a screen display of an application declaration, according toone implementation.

FIG. 5A is a screen display of a mining model class, according to oneimplementation.

FIG. 5B is a screen display of model version details for the miningmodel class shown in FIG. 5A.

FIG. 6A is a screen display of field details for a model class,according to one implementation.

FIG. 6B is a screen display of field details for a model version,according to one implementation.

FIG. 7A is a screen display of a prediction task, according to oneimplementation.

FIG. 7B is a screen display of a field mapping definition according toone implementation.

DETAILED DESCRIPTION

FIG. 1A is a block diagram of a computing system that utilizes multipleversions of a data mining model during execution of analytical tasks,such as prediction tasks. In the implementation shown in FIG. 1A, afront-end software application 100 initiates requests to executeanalytical tasks. The initiation of these requests may result fromvarious events occurring during operation of the front-end softwareapplication 100. The software application 100 sends these requests tothe Analytical Application Provider (AAP) 110. The requests include oneor more input values and a task name. The version selector 131 of theAAP 110 uses the task name to identify a version of a data mining modelto be used when executing the task. For example, the version selector131 may use the task name to reference a definition of a task (such asthe task definition shown later in FIG. 7A) to identify that aparticular mining model version should be used, such as model version136A contained within model repository 134. (Model repository 134 mayreside in a local database of AAP 110 or in a remote data warehouse(such as the local database 116 and the data warehouse 124 shown in FIG.1B)). The AAP 110 then uses a mapping function 130 to map the inputvalues provided by the request from the software application 100 into aset of mapped input values, which are routed to the analytical softwareapplication 132. The analytical software application 132 operates withinan engine 142 (such as a prediction server), which is contained within aset of engines 140. The analytical software application 132 is capableof executing the requested analytical task. To do so, the analyticalsoftware application 132 uses the identified model version, such asmodel version 136A. The analytical software application 132 provides themapped input values for use by model version 136A during execution ofthe analytical task. One or more output values are generated uponexecution of the analytical task, and are passed by the analyticalsoftware application 132 to the AAP 110. The AAP 110 uses the mappingfunction 130 to map these output values into a set of mapped outputvalues, which are routed back to the front-end software application 100.

During run-time operation, new or updated information may be providedfor use with a data mining model during execution of analytical tasks.The new or updated information may include data captured or processedduring the execution of various analytical tasks over a period of time.In these scenarios, a new version of the data mining model can beintroduced into the run-time environment. For example, within the modelrepository 134, a new model version 136B can be introduced and used toreplace the prior model version 136A. During subsequent execution ofanalytical tasks, the new model version 136B is used by the analyticalsoftware application 132 rather than the prior model version 136A. Thesoftware application 100 continues to initiate requests to executeanalytical tasks, and passes input values to the AAP 110. The inputvalues passed by the software application 100 have the same data type,regardless of the version of the data mining model that may be used.That is, the software application 100 passes values for the same set ofinput fields to the AAP 110 whether model version 136A or 136B is used,as shown in the example in FIG. 1A. The software application 100 doesnot need to change its application interface to accommodate input valuesof different data types when different model versions are used duringexecution of the analytical tasks. This allows the software application100 to have a more stable and generic interface with the AAP 110, andreduces the overhead in maintaining software application 100. Inaddition, the software application 100 needs only to provide a taskname, in one implementation, in order for the version selector 131 toidentify the specific model version to be used. The version selector 131uses the task name provided by the software application 100 to referencea task definition that contains the version information. In thisfashion, the software application 100 need not specify the model versionthat is to be used for task execution.

In certain implementations, the mapping functionality 130 containstranslation functionality. In these implementations, the mappingfunctionality 130 is capable of translating input or output valuesbetween the software application 100 and the analytical softwareapplication 132. The translation may be required to convert values of acertain domain and data type into a different domain and data type thatis recognized by the version of the data mining model being used, suchas model version 136A or 136B. For example, the software application 100may provide a value for input field “IN” (e.g., “male”) of data type“string,” and may expect a value for output field “OUT” (e.g., “willchurn”) of data type “string.” The analytical software application 132may expect an input value of “TASK_IN” of data type “integer” (e.g., 0)when using model version 136A during task execution to generate a valuefor output field “TASK_OUT” (e.g., 1) of data type “integer.” In thisscenario, the mapping functionality 130 is capable of translating thestring value of the input field “IN” into an integer value for the inputfield “TASK_IN.” Similarly, the mapping functionality 130 is alsocapable of translating the integer value of the output field “TASK_OUT”into a string value for the output field “OUT.”

In one implementation, the mapping functionality 130 is specified by thedefinition of the analytical task in the request sent from the softwareapplication 100. In this implementation, a designer may specify themapping functionality 130 utilized by the AAP 110 when defining theanalytical task that is requested by the software application 100. Insome implementations, model versions 136A or 136B are represented usingthe Predictive Modeling Markup Language (PMML).

The software application 100 need not be directly coupled to the engine142 and the analytical software application 132, and this providescertain advantages. For example, the software application 100 need notspecify the precise analytical engine and precise model version that areto be used, but need only specify the task to be executed by the AAP110. The task definition in the AAP 110 contains the information of theengine and model version to be used for task execution, which could bechanged dynamically without impact on the software application 100. Thisprovides independence to the software application 100, leading toreduced maintenance costs. The generic API to the AAP 110 allows thesoftware application 100 simply to provide the task name and input data.In addition, the software application 100 need not provide modelversion-specific information as input, because the mapping function 130in the AAP 110 contains the model version-specific mappingfunctionality. This provides additional independence to the application,as the exchange of specific data values can be completely hidden fromthe software application 100. In addition, various different engines andmodel version can be more easily introduced into the system withoutadding extra interface overhead to the software application 100. The AAP110, and its version selector 131 and mapping function 130, manage theengine- and model version-specific details.

As shown in FIG. 1A, requests and responses flow directly between thesoftware application 100 and the AAP 110. In many implementations, abusiness rule engine, such as the business rule engine 108 shown in FIG.1B, couples the software application 100 with the AAP 110. In theseimplementations, the business rule engine 108 passes requests sent fromthe software application 100 directly to the AAP 110. The business ruleengine 108 also passes responses from the AAP 110 to the softwareapplication 100. In addition, the business rule engine 108 also uses theoutput information in the responses sent from the AAP 110 to determineif certain events should be signaled to other rules, or if certainactions should be processed in the software application 100. As part ofthe analytical front-end, the business rule engine 108 providesfunctionality for the business rules that are to be applied. Forexample, the business rule engine 108 may apply certain rules thatinitiate the offering of special discount offers to new or existingcustomers.

FIG. 1B is a block diagram of a computing system that incorporates thecomponents shown in FIG. 1A. In this data processing system, AnalyticalApplication Provider (AAP) 110 couples front-end software applications(such as applications 100, 102, 104, or 106) with analytical softwareapplications on analytical engines, such as prediction servers or keyperformance indicator (KPI) servers, during the execution of analyticaltasks. The analytical engines may be local to AAP 110, or may instead bepart of an analytical back-end. For example, the local predictionengines 112 are local to AAP 110, while the data mining provider 120 andOLAP (online analytical processing) provider 122 are part of theanalytical back-end. Engines 140 shown in FIG. 1A may be containedwithin local prediction engines 112 is some implementations, and may becontained in data mining provider 120 or OLAP provider 122 in otherimplementations. Model repository 134 shown in FIG. 1A may be containedwithin local cache 116 in some implementations, and may be containedwithin data warehouse 124 in others. After analytical tasks have beenexecuted by the corresponding analytical engines, AAP 110 then routesoutput information generated from the execution of these tasks back tofront-end applications 100, 102, 104, or 106.

Data warehouse 124, data mining provider 120, and OLAP provider 122serve as part of an analytical back-end that is coupled to AAP 110 viarealtime connector 114. This analytical back-end may provide a frameworkand storage mechanisms for data mining models or other analytical datastores that are stored externally from AAP 110. These components of theanalytical back-end are coupled to AAP 110 using real-time connector114. Local versions of the data mining models or other data stores maybe stored in local result cache 116 for faster and easier access by AAP110. Decision log 118 is used keep track of the predictions,KPI-lookups, and the rule executions during run time of the system. Theinformation stored in decision log 118 may be viewed by an administratorto analyze various execution results. This information may also be usedto judge the quality of prediction models and rules, and may also be fedback into data warehouse 124 for sophisticated long-term analyses. Basedon these analyses, models may be re-trained, or updated, and rules maybe re-adjusted and be automatically deployed to AAP 110 without impactto the front-end software applications.

In one scenario, a data mining expert may create and update miningmodels with data from a customer knowledge base in data warehouse 124.The data within data warehouse 124 could include customer profiles,historical customer orders, etc. OLAP provider 122 provides directaccess to KPI information derived from customer profiles, historicalcustomer orders, etc. Data mining provider 120 is used for modeldeployment, and data mining provider 120 also provides an interface toAAP 110 for executing remote predictions based on mining models locatedin data warehouse 124. Using real-time connector 114, a mining model canbe exported to AAP 110. In one implementation, the model is in aPMML-compliant format. A PMML-compliant format is one that adheres tothe syntax of the standardized Predictive Modeling Markup Language(PMML). PMML is used to define the components of a model in a standardform that can be interpreted by other computing systems.

In one implementation, real-time connector 114 can also connect tothird-party mining providers, which themselves can export and importmodels and provide predictions based on their local models. Thesethird-party mining providers can be located on local or remote servers.It is not necessary that the system include data warehouse 124, datamining provider 120, OLAP provider 122, and real-time connector 114. Forexample, these components are not needed when the data stores usedduring the execution of analytical tasks are stored in local cache 116and when local engines, such as local prediction engines 112, areutilized.

FIG. 2 is a use-case diagram of design- and run-time scenarios forvarious implementations of the invention. FIG. 2 illustrates various usecases performed by the pictured actors in various design- and run-timescenarios. The use cases shown in FIG. 2 are performed to achievevarious analytical functions in a computer system, such as the systemshown in FIG. 1B.

FIG. 2 first shows various aspects of mining model creation. Modelcreator 228 is responsible for model definition 230, model training 232,model evaluation 234, model annotation 236, and model deployment control238. These use cases typically occur within a data warehouse or abusiness information warehouse (BW). Model definition 230 includes thelogical definition of a mining model that will be used within the systemin terms of the information that will flow into the model. Modeltraining 232 includes updating the model over time as it is used. Modelevaluation 234 includes testing the quality and effectiveness of themodel. Model annotation 236 includes annotating model semantics usingtextual descriptions to precisely describe the “rules” in the model. Theannotations can be related to the entire model, as well as to individualelements of the model such as categories and clusters. Model annotationsplay an important part in allowing an AAP administrator to understandhow a model can be applied for predictions in front-end applications.Model deployment control 238 includes deploying and exporting the modelto AAP 110.

KPI-set creator 240 is responsible for KPI-set definition 242, KPI-setdeployment 244, and KPI-set deployment control 246. KPI's, or keyperformance indicators, are key indicators or figures that can bederived from the data collected in a warehouse, such as data warehouse124. KPI's may include such indicators as customer revenues and profits.KPI's may also contain aggregated customer information or otherpre-calculated information. KPI's may be sorted by user or usercategory. KPI-set definition 242 includes logically defining the KPI'sthat are to be a part of the KPI-set, as well as defining the source ofthe KPI's. KPI-set deployment 244 and deployment control 246 include thedeployment of the KPI-set to AAP 110.

The use cases shown in FIG. 2 include both design- and run-time usecases. At design-time, AAP administrator 200 is responsible forapplication definition 202, model deployment 204, prediction taskdefinition 206, prediction task deployment 208, KPI-set deployment 210,KPI-lookup task definition 212, and KPI-lookup task deployment 214.Model deployment 204 includes model class import 216, model versionimport 218, and model version substitution 220.

Application definition 202 includes defining the scope of the particularCRM application. For example, AAP administrator 200 may define theapplications shown in FIG. 1B, such as Internet sales/service 100,interaction center 102, or mobile sales/service 104. Model deployment204 includes actually deploying of the model to be used within thesystem. In one implementation, deployment is restricted to specificroles. In this implementation, deployment controls may become part ofthe model definition. For example, the deployment of a specific modelcould be restricted to specific users/roles or also to specificapplications. These deployment controls create a deploymentauthorization framework.

As part of model deployment 204, model class import 216 includesimporting or manually defining the model class to be used. Model classesare containers for structurally equivalent models. The fields of modelclasses are a superset of all model fields of model versions belongingto the same class. Model versions are mining models within a modelclass. The model classes that can be used are ones that have beenpreviously defined during model class deployment. In addition toimporting the model class, AAP administrator 200 must also identify andimport the model version, which constitutes model version import 218.The model version contains the most current model information. As timeprogresses, model information needs to be continually updated. As such,newer and more recent model versions may need to be imported into thesystem to substitute the older versions. Therefore, model deployment 204also includes model version substitution. The model class and modelversioning concepts allow an administrator to easily switch betweendifferent model versions by changing the version number, without needingto make completely new specifications for the new model versions. Forexample, mappings for the old model version can be inherited and re-usedfor the new model version, as model versions use the same data formatsand model fields.

Prediction task definition 206 includes defining a prediction task thatis to be deployed by the system. Prediction tasks are used by theapplication at run-time to obtain prediction information from analyticalmodels. Prediction tasks may include prediction engine and mining modeldefinitional information, so that the AAP may properly select thesecomponents for task execution at run time. These tasks may furtherinclude input field value information needed for execution of the tasks.Prediction task deployment 208 includes actual deployment of theprediction task within the application that had previously been definedduring prediction task definition 206. Upon such deployment, theapplication has the capability to implement the prediction tasks later(i.e., at run time).

KPI set deployment 210 includes deployment of the KPI set within anapplication that had been previously defined during KPI set definition242. Upon deployment, the KPI set is available for later use by theapplication at run time. KPI-lookup task definition 212 includesdefining a KPI-lookup task that is to be deployed by the system.KPI-lookup tasks are used by the application at run-time to obtain KPIinformation. KPI sets are originally created by KPI set creator 240, asdescribed earlier. KPI-lookup tasks may include KPI-set definitionalinformation, so that the AAP may properly select the appropriate KPI-setused at run time during task execution. These tasks may further includeinput field value information needed for execution of the tasks. Lastly,KPI-lookup task deployment 214 includes actual deployment of theKPI-lookup task within the application. Upon such deployment, theapplication has the capability to implement the KPI-lookup tasks later(i.e., at run time).

At run-time, prediction task execution 224 and KPI-lookup task execution226 occur while a front-end application, such as application 100, 102,104, or 106 shown in FIG. 1B, processes a transaction with customer 222.In one implementation, customer 222 is involved in a session usingInteraction Center application 102. An Interaction Center is an on-lineinteractive session between a customer and a call-center agent. Thecall-center agent has the ability to answer the customer's questions,and to provide feedback directly to the customer during the on-linesession. Both the customer and call-center agent may use a web-basedinterface to communicate with one another. In another implementation,customer 222 is involved in a session using Internet sales/serviceapplication 100.

Prediction task execution 224 and KPI-lookup task execution 226 areinitiated by requests sent from front-end applications 100, 102, 104, or106. These front-end applications send requests to initiate theanalytical tasks 224 or 226 as a direct result of real-time interactionwith customer 222. Front-end applications 100, 102, 104, or 106determine when requests for analytical tasks 224 or 226 are to beinvoked as a result of the context and state of the transaction withcustomer 222.

KPI-lookup task execution 226 includes executing a run-time KPI-lookuptask. This KPI-lookup task is one that had been previously defined anddeployed at design-time. As noted earlier, KPI-lookup tasks utilize theKPI-sets to lookup KPI information that is sent back to the front-endapplications.

Prediction task execution 224 includes executing a run-time predictiontask. This prediction task is one that had been previously defined anddeployed at design-time. As noted earlier, prediction tasks utilizemining models, such as predictive models. Prediction tasks use real-timeinformation provided by the application to generate prediction resultsas output (e.g., customer attractiveness). In one implementation,prediction tasks also use KPI information (e.g., customer revenue) ingenerating predictions. An application may use the predictive output,along with business rules, to determine if customer 222 will be providedwith special offers, promotions, and the like.

FIG. 3A is a conceptual diagram of an exemplary object model for theAAP. The objects shown in FIG. 3A are included within an exemplaryobject model designed for the AAP. The design shows an implementation ofhow such tasks could be executed in a system such as the one shown inFIG. 1B. FIG. 3A shows how an application interacts with an AAP, such asAAP 110 shown in FIG. 1A, to implement KPI-lookup and prediction tasks.In particular, FIG. 3A shows various mappings between elements within anapplication object to elements used for KPI-lookup and prediction tasks.

FIG. 3A shows application object 300, KPI server 302, KPI set 304,mining server 310, model 312, KPI-lookup tasks 306, and prediction task308. Application object 300 maintains information that can be providedby an application as input for the execution of tasks at run time. KPIserver 302 manages KPI operations and interactions. Therefore, KPIserver 302 keeps driver names for the drivers to connect to the KPIproviders (engines), and user identifications, passwords, etc. as logincredentials for the KPI providers. KPI server 302 manages theseoperations at run time to facilitate the functionality required forKPI-lookup tasks. KPI set 304 includes stored KPI information that canbe retrieved during a KPI-lookup task. Mining server 310 managesprediction operations and model import/export. Therefore, mining server310 keeps driver names for the drivers to connect to the miningproviders (engines), and user identifications, passwords, etc. as logincredentials for the mining providers. Mining server 310 manages theseoperations at run time to facilitate the functionality required forprediction tasks. Model 312 includes stored information for thepredictive model used during a prediction task. In one implementation,model 312 and KPI set 304 represent data stores that are stored locallywithin the AAP, such as AAP 110 shown in FIG. 1A. Mining server 310 andKPI server 302 provide connections to mining providers and KPIproviders. These providers can be local to the AAP (e.g., in the case ofa local prediction engine), or can be connections to remote providers.

As shown in FIG. 3A, application object 300 contains various attributes,or fields. For example, application object 300 may contain a budgetfield, an industry field, a “# of webshop visits” field, anattractiveness field, and a confidence field. These fields include bothinput and output. Input fields are those maintained by applicationobject 300 and used as input for either KPI-lookup or prediction tasks.Output fields are those obtained as output from the KPI-lookup orprediction tasks. The budget and industry fields are input fields. The“# of webshop visits”, attractiveness, and confidence fields are outputfields. The budget field indicates a given budget that applies to agiven industry. The industry field indicates the type of industry (suchas service, manufacturing, or other). These two input fields are used bymodel 312 (during prediction task 308) to help generate predictiveoutput. This predictive output generates the output fieldsattractiveness (high, medium, or none) and confidence level (0-100%).The attractiveness field indicates whether an individual is anattractive candidate, and the confidence field indicates the confidencerating of the prediction. These output fields can be used incoordination with business rules to determine if a given customer willbe given a special offer or promotion. For example, if the customer ispredicted as a highly attractive one with a 75% (or higher) confidencerating, the business rules would indicate that a special promotionshould be offered. The “# of webshop visits” field is also an outputfield. The value of this output field is provided by KPI set 304 toindicate if an individual has visited a webshop frequently, moderately,or rarely. In one implementation, the “# of webshop visits” field mayalso be used as input for prediction task 308.

An operational CRM system implements KPI-lookup tasks and predictiontasks (such as tasks 306 and 308), as shown in the example in FIG. 3A.KPI-lookup task 306 uses KPI server 302 and KPI set 304 and provides forthe run-time functionality of looking up KPI information. This KPIinformation is then sent back to application object 300. This KPIinformation may be used directly by application object 300, or mayadditionally be used as input to a prediction task, such as predictiontask 308.

KPI-lookup task 306 will be initiated by the application in FIG. 3A, andwill use input information as specified in application object 300.Although not shown, application object 300 may provide a customer IDthat will be used by KPI-lookup task 306. In one implementation, thecustomer ID is an input field in application object 300. KPI-lookup task306 uses KPI server 302 to help manage the functionality required forrun-time execution of the task. In addition, KPI-lookup task 306 willuse the input information from application object 300 to obtain therequested KPI information from KPI set 304. In one implementation,KPI-lookup task 306 contains mapping information for use by the AAP totranslate field information in application object 300 to fieldinformation used by KPI set 304. In addition, KPI-lookup task 306 alsocontains mapping information for use by the AAP to translate fieldinformation from KPI set 304 back to application object 300. Thismapping functionality may be required to directly map field elements, orto also possibly convert between differing field data types. Forexample, KPI set 304 maintains a “# of webshop visits” field havingvalues from 0-1000. Application object 300, however, maintains aseparate “# of webshop visits” field having the values of “frequent,”“moderate,” and “rare.” Thus, these separate fields in KPI set 304 andapplication object 300 do not have the same data type. KPI-lookup task306 contains mapping functionality to translate the values from one “#of webshop visits” to the other. For example, the mapping functionalitymay designate that “# of webshop visits” in KPI set 304 having valuesbetween 0-50 map to the value of “rare” within application object 300.Similarly, values between 51-600 may map to the value of “moderate,” andvalues between 601-1000 may map to the value of “frequent.” These andother forms of mapping functionality may be utilized by KPI-lookup task306.

In some implementations, prediction task 308 or KPI-lookup task 306 mayrequire input that is not available to, or provided by, applicationobject 300. In these implementations, the mapping functionality providesthe missing information. This information could include certain defaultvalues or constants. In some implementations, the mapping functionalitydynamically determines the input that is provided to the task based onthe context of the information in application object 300.

Prediction task 308 uses mining server 310 and model 312 to help managethe functionality required for run-time execution of the task.Prediction output information is provided to application object 300,which may later be processed by one or more business rules. At run time,an application initiates prediction task 308 and provides inputinformation, such as budget and industry information. Prediction task308 processes this input information in model 312 in using mining server310. Model 312 is a predictive model that is capable of generatedpredictive output when processed by mining server 310. Model 312 usesthe input information for budget and industry and generates predictiveoutput for an attractiveness category and for confidence. The predictiveoutput is then sent back to application object 300. Prediction task 308also contains mapping information for use by the AAP to map field valuesbetween application object 300 and model 312. For example, bothapplication object 300 and model 312 contain budget and industry fields.These are input fields. In general, input fields may be used to hold awide variety of information, including customer or attributeinformation. However, the field data types often need to mapped to oneanother. In some cases, direct mapping is possible between field values.For example, the industry field values in application object 300(service, manufacturing, and others) can be directly mapped to theindustry field values in model 312 (S, M, O) because these field valueshave substantially the same data types. In other cases, indirectmapping, or conversion, is required. For example, the budget fieldvalues in application object 300 (0-1,000,000) cannot be directly mappedto the budget field values in model 312 (low, medium, high). Therefore,the AAP needs to be capable of translating between these field valuesusing an indirect, or conversion, function. For example, values from0-100,000 may be mapped to “low.” Similarly, values from 100,001-700,000may be mapped to “medium,” and values from 700,001-1,000,000 may bemapped to “high.”

Additionally, both application object 300 and model 312 containpredicted attractiveness category and confidence fields. These areoutput fields. These fields also must be mapped to one another.Prediction task 308 uses model 312 and mining server 310 to generate anattractiveness category of 0, 1, or 2. These must be mapped to theattractiveness field values for application object 300 of high, medium,and none. In one example, an attractiveness category of 0 could bemapped to a value of none, while a category of 2 could be mapped to avalue of high. Prediction task 308 also uses model 312 and server 310 togenerate a confidence of 0 . . . 1. These must be mapped to thepercentages (0-100%) of the confidence field in application object 300.These and other forms of mapping functionality may be utilized by theAAP for prediction task 308.

FIG. 3B is a conceptual diagram of an architectural design for usingmultiple versions of data mining models, according to oneimplementation. FIG. 3B illustrates the concept of model classes andversions. Front-end application 100 initiates a request to executeprediction task 308 to perform a prediction based on a particular datamining model version. To execute a single prediction, front-endapplication 100 uses the prediction application interface (API) ofprediction task 308 shown in FIG. 3A. It uses the API to pass inputvalues for application fields to prediction task 308 for task execution,and upon such execution, front-end application obtains output values forthe prediction output application fields of prediction task 308.

Application object 300, which is also shown in FIG. 3A, contains a listof all application fields (AF1, AF2, . . . , AFn). These applicationfields provide the interface between front-end application 100 andprediction task 308. That is, front-end application 100 sets values forinput application fields, such as fields AF1 and AF2, in applicationobject 300. Front-end application 100 also gets back values for outputapplication fields, such as field AFn, after the processing ofprediction task 308. All further internals and details of predictiontask 308 are transparent to front-end application 100.

The actual real-time prediction is based on a data mining model version.For example, this model version could be a decision tree model, whichwas trained with customer data as of 2002. The various model versionsshown in FIG. 3B are model versions 332, 334, 338, and 340 (whichcorrespond to model versions V01, V02, V11, and V12, respectively). Eachmodel version belongs to a model class. A model class is a group ofmodel versions trained with the same mining function (e.g.,classification, regression, or clustering) and with the same logicaldata. FIG. 3B shows model classes 330 and 336 (which correspond to modelclasses C0 and C1, respectively). Model versions 332 and 334 are eachversions of model class 330. Model versions 338 and 340 are eachversions of model class 336. Model versions of the same model classtypically differ in the selection of the data instances used fortraining. For example, model version 338 could be trained by usingcustomer data as of 2001, while model version 340 could be trained withcustomer data as of 2002. In some implementations, all model versions ofthe same model class have the same model fields, as the model fields arethe result of using the same logical data. Model class 330 includesinput fields MF1 and MF2, and also includes output field MF_(n−1). Modelclass 336 includes the same fields as its parent, or super, model class330, but also includes additional output field MFn.

Models can be arranged in a hierarchy of model classes. For example,model class 336 may be more specific than model class 330, as only asingle additional attribute (MFn) was used to train models in modelclass 336, as compared to model class 330.

The model class and versioning architecture shown in FIG. 3B allows theapplication-oriented organization of mining models for easyadministration. If prediction task 308 is configured to use a givenmodel version, it can easily be re-configured to use another modelversion of the same model class or of all more generic, or super,classes. For example, if prediction task 308 is configured to use modelversion 340 (as shown in FIG. 3B), it can be re-configured to use modelversion 338, model version 332, or model version 334. The reason forthis easy switch is that if an application already provides all inputvalues for the input application fields, and if these values can bepassed to the given model version 340 as prediction input, then allgiven information can also be used to process a prediction on anothermodel version of the same class (such as model version 338), or a modelversion of a model super-class (such as model versions 332 or 334 frommodel class 330).

Additional consideration for the specification of prediction task 308comes from the specification of mappings between application fields andmodel fields. For example, the categorical field values “male” and“female” for the application field AF1 may have to be mapped to thevalues “0” and “1” for model field MF1. The model class/versioningtechnique allows the easy and systematic re-use and specialization ofmappings due to the inheritance of mappings along the class hierarchy.The following mappings can be specified:

-   a. Class-Level Mappings. Mappings can be specified between an    application field and a model class field, even without having a    model version instance for the model class. For example, the task    designer may know that all model versions of the class use “0” and    “1” for a customer gender field MF1 and that the application deals    with values “male” and “female” in application field AF1. In this    case, the task designer can specify a mapping M01 for model class    330, and a mapping M11 for model class 336. Mappings can be    inherited from super-classes and overwritten. For example, if the    mapping M01 already exists, and if the task designer wants to re-use    this mapping for model class 336, the task designer can simply do    so. If, however, the task designer wants to specify a new mapping    for the field MF1, the task designer can overwrite mapping M01 with    mapping M11.-   b. Version-Level Mappings. The most specific mapping is that between    an application field and the field of a model version. For example,    for model version 340, there may be a specific mapping M1 n′ between    application field AFn and model field MFn. All mappings given for    the field of a model class can be inherited by the model versions of    this class. For example, for model field MFn in model version 340,    the model class mapping M1 n could be inherited. The inherited    mappings, however, can also be overwritten by specifying an explicit    version-level mapping M1 n′. This mapping “overwrites” the inherited    mapping M1 n given for model class 336.

Through the application of object-oriented programming concepts to theorganization of mining models, the maintenance of many mining models inthe real-time analytical framework becomes significantly facilitated.The organization of models along a class hierarchy with inheritance ofmappings, and the grouping of structurally equivalent model versions inclasses, allow task designers to “attach” new models to applications,and to switch applications between different model versions with minimumeffort.

FIG. 4 through FIG. 9 show displays of various screens that are used indesigning or creating various components used in a real-time analyticssystem, such as the system shown in FIG. 1B. A user or administrator mayuse the menus and options shown on these screen displays for performingsome of the use cases shown in FIG. 2, such as application definition,model definition, KPI-set definition, prediction task definition,KPI-lookup task definition, and the like. These screen displays areshown for exemplary purposes only.

FIG. 4 is a screen display of a front-end application declaration,according to one implementation of the invention. In thisimplementation, screen display 400 shows an application declaration thatis used during the application definition process, such as applicationdefinition 202 shown in FIG. 2. During this process, an administrator isable to set up a front-end application that is capable of usingreal-time analytics functionality by invoking prediction or KPI-lookuptasks.

Screen display 400 shows a page for application declaration. This pageincludes name field 402, description field 404, import button 406,application fields 408, prediction task button 410, and KPI-lookup taskbutton 412. In the example shown, name field 402 shows that theapplication name is “Internet Sales.” Description field 404 indicatesthat the application is a CRM Internet sales application, such asInternet sales/service application 100 shown in FIG. 1B. Import button406 allows a user to import metadata into the application declarationautomatically, thereby relieving the user of having to manually enterthe information. In one implementation, this is achieved by selection ofa specification, such as a business object specification, that has beenpreviously registered into the AAP. When a user, such as anadministrator, imports this specification, all attributes areautomatically imported into the declaration application.

Application fields 408 specify the specific processing fields used bythe application at run time. Each application field has a name, anin/out designation, and a data type. The name is a unique name withinthe set of application fields 408. The in/out designation specifieswhether an application field is used as input to a prediction orKPI-lookup task, or whether the field is used for output generated bythe prediction or KPI-lookup task and sent back to the application. Thedata type indicates the type of data stored in the application field asa value. The data types shown in FIG. 4 are date, string, and real(i.e., floating point).

Prediction task button 410 and KPI-lookup button 412 are used by theadministrator to create real-time tasks that are to be associated withthe application. The administrator may select button 410 to create aprediction task and button 412 to create a KPI-lookup task. At run-time,after an application has been defined in the AAP, mining models can beused to allow the application to perform prediction, and KPI sets can beused to allow the application to perform KPI lookups as well.

FIG. 5A is a screen display of a mining model class, according to oneimplementation of the invention. In this implementation, screen display500 shows the details of a mining model class that has been eithermanually specified by an AAP administrator or that has beenautomatically created by the AAP when a model version has been deployedfor the model class. An AAP administrator may manually specify the modelclass if the set of fields is known. Alternatively, the AAP is able toautomatically define the model class when it imports a version of themodel class. The fields can be derived from the model version and usedfor the specification of the model class.

Screen display 500 shows a page for the details of a model class. Screendisplay 500 includes class name field 502, classification field 504,description field 506, version field 508, prediction input fields 510,and prediction output fields 514. As shown in the example in FIG. 5A,class name field 502 indicates that the name of the mining model classis “MyCustClas.” Classification field 504 indicates that the model classis used for the classification of customers. Description field 506provides the high-level description of the model class. This descriptionis entered by the model creator. Version field 508 indicates the numberof different versions that exist for the model class. A model class canhave one or more versions. Later versions of the class may contain morespecific or up-to-date information. The model class shown in FIG. 5A hastwo different versions.

Prediction input fields 510 and prediction output fields 514 indicatethe input and output fields that are used for prediction by the miningmodel. The mining model obtains values for the input fields from theapplication to generate predictive output. This predictive output iscaptured in the prediction output fields and sent back to theapplication. As shown in FIG. 5A, the prediction input fields 510include CUSTOMER_AGE, CUSTOMER_GENDER, CUSTOMER_ORDERS, andCUSTOMER_REVENUE. The values for these fields originate in theapplication and are provided to the model class through the execution ofprediction tasks, in one implementation. The prediction output fields514 include the PREDICTED_CLASS field. The value of this field is sentback to the application after the prediction has been generated.

Details buttons are used for providing detailed information about thefields. The model creator may select one of these buttons to view orenter detailed information about prediction input fields 510 or aboutprediction output fields 514.

FIG. 5B is a screen display of model version details for the miningmodel class shown in FIG. 5A. The model shown in the example of FIG. 5Bis a version of the model that was earlier described in FIG. 5A. Anadministrator is capable of defining one or more versions of a miningmodel. In one implementation, all model versions have a compliant set oflogical attributes. That is, the fields of a model version are a subsetof the model class fields, and the data type of the model version fieldis the same or a more specific one than that of the model class. Forexample, if the data type of the model class field CUSTOMER_AGE is aninteger, then the data type of a model version field of CUSTOMER_AGE maybe a real number.

In screen display 530 shown on FIG. 5B, field 532 indicates the name ofthe specific model version, and field 534 provides a brief descriptionof the version. Version field 536 indicates the version number, anddeployment time field 538 provides the date and time to indicate whenthe model version was deployed. By looking at these fields, anadministrator is able to determine how current a given model version is.Training time field 540 indicates when the model version was trained,and field 542 provides information to indicate who deployed the modelversion. Description field 544 provides a more detailed description ofthe model version. In the example shown in FIG. 5B, description field544 indicates that the model version is based on a more accuratecustomer base as of 2001 and includes non-European customers.

In FIG. 5B, prediction input fields 546 are a subset of those shown fromfields 510 in FIG. 5A. Notice that prediction input fields 546 includeonly CUSTOMER_AGE, CUSTOMER_ORDERS, and CUSTOMER_REVENUE. They do notinclude CUSTOMER_GENDER, which is included in the set of fields 510 ofFIG. 5A. Instead, the CUSTOMER_GENDER field is included in the set ofsupplementary fields 548. In one implementation, supplementary fields548 are not necessary, as input, to the prediction process. In thisimplementation, supplementary fields 548 are still included in thedefinition, and mapping functionality for these fields is stillprovided. The reason for this is that supplementary fields 548 maybecome required fields for the prediction task in the next version ofthe model used for the predictions, and this facilitates the dynamicsubstitution of one model version to the next. This structuredemonstrates that a model version may have a slightly differentorganization than its model class. FIG. 5B shows that the model versioncontains the same set of prediction output (i.e., result) fields 514 asthe model class.

Button 550 is used for showing all prediction tasks that are associatedwith the given model version. In addition, button 552 may be selectedfor creating a new prediction task to be associated with the modelversion. These prediction tasks are also associated with the hostapplication, according to one implementation.

FIG. 6A is a screen display of field details for a model class,according to one implementation of the invention. FIG. 6A shows thedetails for the prediction input field of CUSTOMER_AGE that was shown inFIG. 5A. In one implementation, a model creator selects one of thedetails buttons to bring up the page shown in screen display 600 to viewor entered detailed information about this input field.

Screen display 600 shows a page having various fields. These includeclass reference field 602, classification field 604, version field 606,version description field 608, prediction reference field 610, datadescription field 612, model type fields 614 and 616, data type field618, and general description field 620. Class reference field 602 showsthe mining model class with which the prediction field is associated. Inthe example shown, the associated class is “My Mining Model.”Classification field 604 refers to the classification used for theclass.

Version field 606 shows the class version being utilized. As describedearlier, a mining model class may have one or more versions. The versionshown in FIG. 6A is “WW_(—)2001,” which is used for the classificationof World Wide customers in 2001, as indicated by version descriptionfield 608. Prediction reference field 610 indicates the name of theprediction field for which details are provided. As shown, the field isthe CUSTOMER_AGE prediction input field, and data description field 612indicates that this field designates the age of customers in the yearrange [1 . . . 200]. Model type fields 614 and 616 specify the modeltype for the model class. In the example shown in FIG. 6A, the model isone defined using the Predictive Modeling Markup Language (PMML), andthe PMML types are continuous and non-cyclic. Data type field 618indicates that the CUSTOMER_AGE field contains integer values. Lastly,general description field 620 provides a brief general description ofthe CUSTOMER_AGE field.

FIG. 6B is a screen display of field details for a model version,according to one implementation. In this implementation, a screendisplay 630 shows the field details for a specific field in a modelversion that is based on the field for the model class shown in FIG. 6A.A user, such as a designer, can create these field details for a givenmodel version. The screen display 630 shows the class reference field602, a version identifier field 634, a training time field 636, theprediction reference field 610, the data description field 612, and afield description field 640. Each of these fields provide informationabout a particular element used in the model version.

As noted in FIG. 6A, the class reference field 602 shows the miningmodel class for the model version. Each model version contains areference to its base model class. The version identifier field 634shows the unique version number associated with the model version. Thetraining time field 636 shows the exact time when the model version wastrained, or updated, with the new field information. The predictionreference field 610 shows the name of the prediction input field (whichrefers to the same field name shown for the model class in FIG. 6A), andthe data description field 612 shows the high-level description of thefield. Lastly, the field description field 640 shows a more detaileddescription of the field for the model version. In the model versionshown in FIG. 6B, the field description field 640 indicates that themean age of the customers (in the “CUSTOMER_AGE” field) is approximately35, and only a few customers are younger than 20. In this fashion, thefield description field 640 is capable of providing information about afield that is particular to the model version.

FIG. 7A is a screen display of a prediction task, according to oneimplementation of the invention. In this implementation, screen display700 shows how an administrator, such as AAP administrator in FIG. 2, isable to define a prediction task. A prediction task is an analyticaltask, in one implementation. A prediction task is initiated by anapplication, such as an Internet sales application, to obtain predictiveoutput. The prediction task has a format that includes a set of inputand output fields. The application initiates the task by sending arequest to the AAP using a real-time task interface. The predictiveoutput is then used by the application to initiate subsequent events,such as offering a special promotion to a valued customer. A system,such as AAP 110 shown in FIG. 1A, processes the information contained inthe prediction task to help determine the logistics for executing thetask. For example, AAP 110 is able to use the information provided inthe prediction task to identify the mining model class and predictionengine that are to be used in executing the task. AAP 110 is also ableto identify the application and prediction fields that are used for taskexecution, and the pertinent value mappings between such fields.

In FIG. 7A, screen display 700 shows a page for defining a predictiontask. The page contains various fields. An administrator can use thesefields to enter, review, and revise the definition of the predictiontask. Name field 702 indicates the name (or identifier) of theprediction task. The administrator may select button 704 to change thecontents of name field 702. Name description field 706 provides a briefdescription of the name of the prediction task. Application field 708indicates the type of application that will be utilizing the predictiontask. As shown in the example in FIG. 7A, the application is an Internetsales application. Application description field 710 provides a briefdescription of the application.

Model class field 712 indicates the name of the mining model class thatwill be used to implement the predictions. Model class description field714 provides a brief description of the model class that is used.Version field 716 indicates the version number of the mining modelspecified in model class field 712. There may be one or more versions ofthe model, and version field 716 specifies which version will be used bythe prediction task. As shown in FIG. 7A, version field 716 indicatesthat version “2” corresponding to “WW_(—)2001” is to be used. Versiondescription field 718 provides a brief description of the version.Prediction engine field 720 indicates the prediction engine that will beused for generating the predictive output. The prediction task uses themining model in the prediction engine to generate this output. Theprediction engine may be either a local or remote engine. Enginedescription field 722 provides a brief description of the predictionengine that is used.

Prediction input fields 724 are those set of fields used as input to theprediction process. Typically, the values for these fields are providedby the application, such as an Internet sales application. These inputfields provide the mining model with the information that is used togenerate predictions. As shown, the input fields are CUSTOMER_AGE,CUSTOMER_GENDER, CUSTOMER_ORDERS, and CUSTOMER_REVENUE. Although thevalues for these fields are provided by the application, there is notalways a direct mapping of the fields that are maintained by theapplication and those maintained by the mining model. For example,application fields 726 do not have the same field names (or value types,in some cases) as prediction input fields 724. Therefore, in someinstances, a mapping function is utilized. This mapping function isincluded within the scope of the prediction task. To give an example,the value of the application field of BIRTH_DATE is mapped to an age asspecified by the CUSTOMER_AGE prediction input field. The predictiontask uses the birth date to determine a current age. This type ofmapping utilizes a conversion function. The mapping function does notrequire any conversion in some instances. For example, the applicationfield of SHOPPER_GENDER can be directly mapped to the CUSTOMER_GENDERprediction input field. All of application fields 726 are mapped in somefashion to prediction input fields 724 within the prediction task.

Prediction output fields 728 contain values that are generated as aresult of prediction processes. As shown in the example in FIG. 7A,these fields include the PREDICTED_CLASS and CONFIDENCE fields. Thevalue for these fields are sent back to the application as predictiveoutput. However, the application has a separate set of output fields 730to capture this predictive output. Therefore, the prediction task alsohas a mapping functionality to map prediction output fields 728 tooutput fields 730 for the application. Note that the prediction outputfield of CONFIDENCE has no corresponding output field used by theInternet sales application in the example shown in FIG. 7A.

Application fields 726 include KPI buttons in one implementation of theinvention. In this implementation, a prediction task can be combinedwith a KPI-lookup task. This is done when a KPI is used as an input tothe prediction process. Thus, KPI buttons are provided for eachapplication field that is used for prediction input. If an administratorselects this button, a KPI-lookup task is selected for delivering a KPI,and the delivered KPI will be assigned to the model field. This type ofassignment creates an automatic invocation of the KPI-lookup task as aprerequisite to the prediction task. As shown in FIG. 7A, theREVENUE_LAST YEAR field will be the result of a KPI-lookup task if theadministrator has selected the KPI button located to the right of thisfield. In this case, the results of the KPI-lookup task will be mappedto the CUSTOMER_REVENUE prediction input field. Any input valuesrequired for a given KPI-lookup task are also listed as part of theprediction task as well, according to one implementation. In thisimplementation, all input values for the KPI-lookup and prediction tasksare grouped together and provided in a unified set of input values.

In one implementation, an application can easily switch between modelversions simply by changing the version number, without specifying a newmapping between the application and the model version. If a predictiontask gets switched to another version, it inherits the mappings betweenapplication fields 726 and prediction input fields 724, and alsoinherits the mappings between prediction output fields 728 and fields730. These mappings can be overridden, or changed, to consider thespecifics of the model version. For example, if the new model versionhas fewer fields than the previous model version, then the mappings canbe changed accordingly.

FIG. 7B is a screen display of a field mapping definition according toone implementation of the invention. FIG. 7B shows how one of theprediction input fields from set 724 (shown in FIG. 7A) is mapped to oneof the application fields from set 726. Screen display 750 shows fields708, 710, 712, 714, 716, and 718 from FIG. 7A. In addition, FIG. 7Bshows a specific prediction input field 752, CUSTOMER_GENDER, and aspecific application field 754, SHOPPER_GENDER. As described earlier,input fields such as these may often utilize a mapping function. In theexample shown in FIG. 7B, values 756 are mapped to values 758. In thisexample, ‘Male’ from values 756 is mapped to ‘0’ in values 758. ‘Female’from values 756 is mapped to ‘1’ in values 758. This is just one exampleof a mapping functionality that may be utilized by the prediction task.For example, other integer, real, enumerated, etc., types may be usedfor the mapping function.

A number of implementations of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

1. A computer system, including a processor, that allows front-endsoftware applications to use multiple versions of a data mining modelduring execution of analytical tasks, the computer system comprising: afront-end software application operable to generate transaction data andsend task requests to an analytical processing front-end application, atask request for analytical processing including a predefined task nameand input data for a data mining model, the front-end softwareapplication being one of multiple front-end software applicationsoperable to send task requests for analytical processing to theanalytical processing front-end application; the analytical processingfront-end application comprising: a model version selection module thatis operable to use a predefined task name in a task request receivedfrom the front-end software application to identify, from predefinedtask definition information, a specific version of the data mining modelto be used during execution of an analytical task, the specific versionof the data mining model being included in an analytical back-endapplication, the analytical back-end application being distinct from theanalytical processing front-end application and including multipleversions of the data mining model; and a mapping module that is operableto map, in accordance with predefined mapping definitions included inthe predefined task definition information, the input data into a formatusable by the specific version of the data mining model, and theanalytical processing back-end application operable to invoke executionof the analytical task using the specific version of the data miningmodel and the mapped input data to generate output data.
 2. The computersystem of claim 1, wherein the mapping module is operable to map inputdata included in the task request into a format usable by any version ofthe data mining model.
 3. The computer system of claim 1, wherein themapping module is further operable to map output data generated uponexecution of the analytical task into a format usable by the front-endsoftware application.
 4. The computer system of claim 1, wherein theanalytical task is a prediction task.
 5. A computer-implemented methodfor providing a software interface to multiple versions of a data miningmodel during execution of analytical tasks, the method comprising:obtaining from a front-end software application a first task request foranalytical processing, the first task request containing a first set ofinput values and a predefined task name, the front-end softwareapplication being one of multiple front-end software applicationsoperable to send task requests for analytical processing to the softwareinterface; using the predefined task name to identify, from predefinedtask definition information, a first version of the data mining model tobe used when executing a first analytical task; using a first inputmapping function to map, in accordance with predefined mappingdefinition information included in the predefined task definitioninformation, the first set of input values into a first set of mappedinput values for use by an analytical software application whenexecuting the first analytical task with the first version of the datamining model; obtaining from the front-end software application a secondtask request for analytical processing, the second task requestcontaining a second set of input values and the predefined task name,and the second set of input values being a subset of the first set ofinput values; using the predefined task name to identify, frompredefined task definition information, a second version of the datamining model to be used; and using a second input mapping function tomap, in accordance with predefined mapping definition informationincluded in the predefined task definition information, the second setof input values into a second set of mapped input values for use by theanalytical software application when executing the second analyticaltask with the second version of the data mining model.
 6. Thecomputer-implemented method of claim 5, wherein each one of the secondset of input values has a data type that matches a data type of one ofthe input values from the first set of input values.
 7. Thecomputer-implemented method of claim 5, wherein the method furthercomprises: sending a first set of output values generated upon executionof the first analytical task to the front-end software application; andsending a second set of output values generated upon execution of thesecond analytical task to the front-end software application.
 8. Thecomputer-implemented method of claim 7, wherein sending a second set ofoutput values generated upon execution of the second analytical task tothe front-end software application includes sending a second set ofoutput values that are a subset of the first set of output values. 9.The computer-implemented method of claim 8, wherein sending a second setof output values that are a subset of the first set of output valuesincludes sending a second set of output values that each individuallyhave a data type that matches a data type of one of the output valuesfrom the first set of output values.
 10. The computer-implemented methodof claim 7, wherein: sending a first set of output values generated uponexecution of the first analytical task to the front-end softwareapplication includes using a first output mapping function to map thefirst set of output values into a first set of mapped output values foruse by the front-end software application; and sending a second set ofoutput values generated upon execution of the second analytical task tothe front-end software application includes using a second outputmapping function to map the second set of output values into a secondset of mapped output values for use by the front-end softwareapplication.
 11. The computer-implemented method of claim 10, whereinthe second output mapping function is identical to the first outputmapping function.
 12. The computer-implemented method of claim 5,wherein the second input mapping function is identical to the firstinput mapping function.
 13. The computer-implemented method of claim 5,wherein the first and second analytical tasks are prediction tasks. 14.The computer system of claim 1 wherein the analytical processingfront-end comprises a business rule engine operable to use output datagenerated by the analytical processing back-end application to determinewhether an event should be triggered in the front-end softwareapplication.
 15. The method of claim 5 further comprising using businessrules and output resulting from executing the first analytical task withthe first version of the data mining model to determine whether an eventshould be triggered in the front-end software application.